CCNA AI and Network Operations Practice Question
A network engineer at a large enterprise observes repeated spikes in latency on the core network every weekday at 10:00 AM, but no corresponding increase in overall bandwidth utilization. The engineer wants to use AI/ML to automatically identify the root cause and take corrective action without manual intervention. Which concept best describes this approach?
⚠ Common exam trap
Cisco often tests the distinction between a single AI/ML technique (like anomaly detection) and the full closed-loop automation framework (IBN), leading candidates to pick the narrower answer when the question explicitly requires both detection and automated corrective action.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Intent-based networking
Intent-based networking (IBN) is correct because it describes a closed-loop system where the network continuously validates that its operational state matches the desired business intent. In this scenario, the engineer wants the network to automatically detect the latency anomaly, correlate it with other telemetry (e.g., routing changes, queue drops), and take corrective action (e.g., reroute traffic, adjust QoS) without human intervention — which is the core promise of IBN, often implemented via Cisco's DNA Center with Assurance and AI/ML capabilities.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Anomaly detection
Why it's wrong here
Anomaly detection is a monitoring function that flags statistically unusual behavior — such as a latency spike — using thresholds, baselines, or ML models, but it stops at alerting. It does not include the closed-loop policy enforcement needed to automatically modify network configuration. In this scenario, the system must both detect the deviation and reconfigure the network to restore the intended state, which anomaly detection alone cannot perform.
- ✓
Intent-based networking
Why this is correct
Intent-based networking (IBN) uses closed-loop automation to continuously monitor the network, detect when the actual state deviates from the intended state (e.g., latency spikes), and automatically reconfigure the network to restore the intent. This matches the scenario of automatic identification and correction.
- ✗
Predictive analytics
Why it's wrong here
Predictive analytics focuses on the future, not the present: it ingests historical and streaming data to forecast events like link failure or congestion before they happen. It supports proactive planning but does not react to an existing anomaly in real time. The scenario describes an automatic response to a current latency spike, which is a reactive, closed-loop action rather than a future-oriented prediction.
- ✗
Machine learning classification
Why it's wrong here
Machine learning classification is a specific technique that categorizes data points into classes (for example, labeling traffic as normal or anomalous) but it is not a complete automation system. A classifier produces a label or score; it lacks the intent, policy, and actuation mechanisms to determine the appropriate corrective action and apply it to the network. Therefore, it could serve as a component inside an intent-based system, but by itself it cannot both identify and correct the problem.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The 200-301 exam frequently reuses these exact scenarios with slightly different constraints.
✓Intent-based networkingCorrect answer▾
Why this is correct
Intent-based networking (IBN) uses closed-loop automation to continuously monitor the network, detect when the actual state deviates from the intended state (e.g., latency spikes), and automatically reconfigure the network to restore the intent. This matches the scenario of automatic identification and correction.
✗Anomaly detectionWrong answer — click to see why▾
Why this is wrong here
Anomaly detection identifies unusual patterns like latency spikes, but it does not include automatic corrective action. The scenario requires both detection and automated response, which anomaly detection alone cannot provide.
Why candidates choose this
Students may think anomaly detection is sufficient because it can identify the latency spikes, but they overlook the requirement for automatic corrective action without manual intervention.
✗Predictive analyticsWrong answer — click to see why▾
Why this is wrong here
Predictive analytics forecasts future events (e.g., predicting when a link will fail), but it does not automatically take corrective action. The scenario involves detecting and correcting an existing anomaly, not predicting a future one.
Why candidates choose this
Students might confuse predictive analytics with proactive detection, but the scenario describes an ongoing issue that needs immediate correction, not prediction of future events.
✗Machine learning classificationWrong answer — click to see why▾
Why this is wrong here
Machine learning classification categorizes data (e.g., classifying traffic as normal or anomalous), but it does not inherently include automated corrective actions. The scenario requires a system that both detects and corrects.
Why candidates choose this
Students may think classification can identify the root cause, but classification alone does not trigger automated corrective actions; it only labels data.
Analysis generated from the official 200-301blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Agentic AI in Network Operations
Key term
Latency
Latency is the time delay between a request being sent over a network and the response being received, often measured in milliseconds.
Key term
QoS
QoS (Quality of Service) is a network technique that manages data traffic to ensure critical applications get the bandwidth and low latency they need.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This 200-301 practice question is part of Courseiva's free Cisco certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the 200-301 exam.